Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer's disease

Osteoporosis and Alzheimer´s disease (or Alzheimer´s disease and Osteoporosis)

Alzheimer's disease (AD) and osteoporosis are two diseases that mainly affect elderly people, with increases in the occurrence of cases due to a longer life expectancy. Several epidemiological studies have shown a reciprocal association between both diseases, finding an increase in incidence of osteoporosis in patients with AD, and a higher burden of AD in osteoporotic patients. This epidemiological relationship has motivated the search for molecules, genes, signaling pathways and mechanisms that are related to both pathologies. The mechanisms found in these studies can serve to improve treatments and establish better patient care protocols.

Association between Age at Diagnosis of Hyperlipidemia and Subsequent Risk of Dementia

OBJECTIVES

The relationship between age at diagnosis of hyperlipidemia and dementia remains unclear. We examined whether younger age at diagnosis of hyperlipidemia is associated with higher risk of subsequent dementia.

DESIGN

A longitudinal population-based study with a median follow-up of 12.8 years.

SETTING AND PARTICIPANTS

We analyzed data on a sample of 489,642 participants in the United Kingdom.

METHODS

This study was based on the UK Biobank. Information on hyperlipidemia and dementia diagnoses was collected at baseline (2006-2010) and follow-up [median = 12.8 years, interquartile range (IQR): 12.1-13.6 years]. Propensity score matching method and Cox proportional hazards models were used to assess the association between age at diagnosis of hyperlipidemia and dementia.

RESULTS

Among 489,642 participants (mean age: 56.9 ± 8.1 years; female: 54.7%), 114,112 (23.3%) were diagnosed with hyperlipidemia. Younger age at diagnosis of hyperlipidemia (per 10-year decrease) was significantly associated with higher risks of all-cause dementia [hazard ratio (HR), 1.12; 95% CI, 1.07-1.18; P < .001], Alzheimer's disease (AD) (HR, 1.22; 95% CI, 1.14-1.31; P < .001), and vascular dementia (VD) (HR, 1.16; 95% CI, 1.05-1.27; P < .001). After propensity score matching, patients with hyperlipidemia diagnosed before 50 years had the highest HR for all-cause dementia (HR, 1.46; 95% CI, 1.15-1.86; P = .002), followed by patients diagnosed between 50 and 69 years (HR, 1.21; 95% CI, 1.12-1.31; P < .001) and then patients diagnosed aged 70 years and older (HR, 0.94; 95% CI, 0.84-1.06; P = .302). Similar results were observed for AD and VD.

CONCLUSIONS AND IMPLICATIONS

A dose-response relationship between age at hyperlipidemia diagnosis and risk of dementia was found in the longitudinal cohort study, with younger age at diagnosis of hyperlipidemia being associated with higher subsequent risk.

Reduced GLP-1R availability in the caudate nucleus with Alzheimer's disease

The glucagon-like peptide-1 receptor (GLP-1R) agonists reduce glycated hemoglobin in patients with type 2 diabetes. Mounting evidence indicates that the potential of GLP-1R agonists, mimicking a 30 amino acid ligand, GLP-1, extends to the treatment of neurodegenerative conditions, with a particular focus on Alzheimer's disease (AD). However, the mechanism that underlies regulation of GLP-1R availability in the brain with AD remains poorly understood. Here, using whole transcriptome RNA-Seq of the human postmortem caudate nucleus with AD and chronic hydrocephalus (CH) in the elderly, we found that GLP-1R and select mRNAs expressed in glucose dysmetabolism and dyslipidemia were significantly altered. Furthermore, we detected human RNA indicating a deficiency in doublecortin (DCX) levels and the presence of ferroptosis in the caudate nucleus impacted by AD. Using the genome data viewer, we assessed mutability of GLP-1R and 39 other genes by two factors associated with high mutation rates in chromosomes of four species. Surprisingly, we identified that nucleotide sizes of GLP-1R transcript exceptionally differed in all four species of humans, chimpanzees, rats, and mice by up to 6-fold. Taken together, the protein network database analysis suggests that reduced GLP-1R in the aged human brain is associated with glucose dysmetabolism, ferroptosis, and reduced DCX+ neurons, that may contribute to AD.

Obesity aggravates neuroinflammatory and neurodegenerative effects of bisphenol A in female rats

Bisphenol A (BPA), a common plasticizer, is categorized as a neurotoxic compound. Its impact on individuals exhibits sex-linked variations. Several biological and environmental factors impact the degree of toxicity. Moreover, nutritional factors have profound influence on toxicity outcome. BPA has been demonstrated to be an obesogen. However, research on the potential role of obesity as a confounding factor in BPA toxicity is lacking. We studied the neurodegenerative effects in high-fat diet (HFD)-induced obese female rats after exposure to BPA (10 mg/L via drinking water for 90 days). Four groups were taken in this study - Control, HFD, HFD + BPA and BPA. Cognitive function was evaluated through novel object recognition (NOR) test. Inflammatory changes in brain, and changes in hormonal level, lipid profile, glucose tolerance, oxidative stress, and antioxidants were also determined. HFD + BPA group rats showed a significant decline in memory function in NOR test. The cerebral cortex (CC) of the brain showed increased neurodegenerative changes as measured by microtubule-associated protein-2 (MAP-2) accompanied by histopathological confirmation. The increased level of neuroinflammation was demonstrated by microglial activation (Iba-1) and protein expression of nuclear factor- kappa B (NF-КB) in the brain. Obesity also caused significant (p < 0.05) increase in lipid peroxidation accompanied by reduced activities of antioxidant enzymes (glutathione S-transferase, catalase and glutathione peroxidase) and decrease in reduced-glutathione (p < 0.05) when compared to non-obese rats with BPA treatment. Overall, study revealed that obesity serves as a risk factor in the toxicity of BPA which may exacerbate the progression of neurological diseases.

Depression and cognition are associated with lipid dysregulation in both a multigenerational study of depression and the National Health and Nutrition Examination Survey

Chronic dysregulation of peripheral lipids has been found to be associated with depression and cognition, but their interaction has not been investigated. Growing evidence has highlighted the association between peripheral lipoprotein levels with depression and cognition with inconsistent results. We assessed the association between peripheral lipids, depression, and cognition while evaluating their potential interactions using robust clinically relevant predictors such as lipoprotein levels and chronic medical disorders that dysregulate lipoproteins. We report an association between peripheral lipids, depression, and cognition, suggesting a common underlying biological mechanism driven by lipid dysregulation in two independent studies. Analysis of a longitudinal study of a cohort at high or low familial risk for major depressive disorder (MDD) (n = 526) found metabolic diseases, including diabetes, hypertension, and other cardiovascular diseases, were associated with MDD and cognitive outcomes. Investigating a cross-sectional population survey of adults in the National Health and Nutrition Examination Survey 2011-2014 (NHANES) (n = 2377), depression was found to be associated with high density lipoprotein (HDL) and cognitive assessments. In the familial risk study, medical conditions were found to be associated with chronic lipid dysregulation and were significantly associated with MDD using the structural equation model. A positive association between chronic lipid dysregulation and cognitive scores was found in an exploratory analysis of the familial risk study. In a complementary study, analysis of NHANES revealed a positive association of HDL levels with cognition. Further analysis of the NHANES cohort indicated that depression status mediated the interaction between HDL levels and cognitive tests. Importantly, the protective effect of HDL on cognition was absent in those with depressive symptoms, which may ultimately result in worse outcomes leading to cognitive decline. These findings highlight the potential for the early predictive value of medical conditions with chronic lipid dyshomeostasis for the risk of depression and cognitive decline.

Role of ketogenic diet in neurodegenerative diseases focusing on Alzheimer diseases: The guardian angle

The ketogenic diet (KD) is a low-carbohydrate, adequate protein and high-fat diet. KD is primarily used to treat refractory epilepsy. KD was shown to be effective in treating different neurodegenerative diseases. Alzheimer disease (AD) is the first common neurodegenerative disease in the world characterized by memory and cognitive impairment. However, the underlying mechanism of KD in controlling of AD and other neurodegenerative diseases are not discussed widely. Therefore, this review aims to revise the fundamental mechanism of KD in different neurodegenerative diseases focusing on the AD. KD induces a fasting-like which modulates the central and peripheral metabolism by regulating mitochondrial dysfunction, oxidative stress, inflammation, gut-flora, and autophagy in different neurodegenerative diseases. Different studies highlighted that KD improves AD neuropathology by regulating synaptic neurotransmission and inhibiting of neuroinflammation and oxidative stress. In conclusion, KD improves cognitive function and attenuates the progression of AD neuropathology by reducing oxidative stress, mitochondrial dysfunction, and enhancing neuronal autophagy and brain BDNF.

Associations of Triglycerides and Atherogenic Index of Plasma with Brain Structure in the Middle-Aged and Elderly Adults

Triglyceride (TG) and atherogenic index of plasma (AIP) have been acknowledged to be risk factors for vascular insults, but their impacts on the brain system remain elusive. To fill in some gaps, we investigated associations of TG and AIP with brain structure, leveraging the UK Biobank database. TG and high-density lipoprotein cholesterol (HDL-C) were examined at baseline and AIP was calculated as log (TG/HDL-C). We build several linear regression models to estimate associations of TG and AIP with volumes of brain grey matter phenotypes. Significant inverse associations of TG and AIP with volumes of specific subcortical traits were observed, among which TG and AIP were most significantly associated with caudate nucleus (TG: β [95% confidence interval CI] = -0.036 [-0.051, -0.022], AIP: -0.038 [-0.053, -0.023]), thalamus (-0.029 [-0.042, -0.017], -0.032 [-0.045, -0.019]). Higher TG and AIP were also considerably related with reduced cortical structure volumes, where two most significant associations of TG and AIP were with insula (TG: -0.035 [-0.048, -0.022], AIP: -0.038 [-0.052, -0.025]), superior temporal gyrus (-0.030 [-0.043, -0.017], -0.033 [-0.047, -0.020]). Modification effects of sex and regular physical activity on the associations were discovered as well. Our findings show adverse associations of TG and AIP with grey matter volumes, which has essential public health implications for early prevention in neurodegenerative diseases.

Important roles of linoleic acid and α-linolenic acid in regulating cognitive impairment and neuropsychiatric issues in metabolic-related dementia

Metabolic syndrome (MetS), which is characterized by insulin resistance, high blood glucose, obesity, and dyslipidemia, is known to increase the risk of dementia accompanied by memory loss and depression. The direct pathways and specific mechanisms in the central nervous system (CNS) for addressing fatty acid imbalances in MetS have not yet been fully elucidated. Among polyunsaturated acids, linoleic acid (LA, n6-PUFA) and α-linolenic acid (ALA, n3-PUFA), which are two essential fatty acids that should be provided by food sources (e.g., vegetable oils and seeds), have been reported to regulate various cellular mechanisms including apoptosis, inflammatory responses, mitochondrial biogenesis, and insulin signaling. Furthermore, inadequate intake of LA and ALA is reported to be involved in neuropathology and neuropsychiatric diseases as well as imbalanced metabolic conditions. Herein, we review the roles of LA and ALA on metabolic-related dementia focusing on insulin resistance, dyslipidemia, synaptic plasticity, cognitive function, and neuropsychiatric issues. This review suggests that LA and ALA are important fatty acids for concurrent treatment of both MetS and neurological problems.

Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study

BACKGROUND

Blood biomarkers have the potential to transform Alzheimer's disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored.

OBJECTIVE

This study aims to enhance blood biomarkers' sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration's efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features.

METHODS

We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity.

RESULTS

The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance.

CONCLUSIONS

In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.